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This entry is from Summer semester 2018 and might be obsolete. You can find a current equivalent here.

CS 516 — Content-based Image and Video Analysis
(dt. Inhaltsbasierte Bild- und Videoanalyse)

Level, degree of commitment Specialization module, compulsory elective module
Forms of teaching and learning,
Lecture (2 SWS), recitation class (2 SWS),
180 hours (60 h attendance, 120 h private study)
Credit points,
formal requirements
6 CP
Course requirement(s): Oral or written examination
Examination type: Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Computer Science.
One semester,
Im Wechsel mit anderen specialization moduleen
Person in charge of the module's outline Prof. Dr. Bernd Freisleben, Dr. Markus Mühling


The lecture deals with methods for content-based image processing

and video analysis. The following topics will be covered:

  • Basics of image and video processing
  • Machine learning
  • Basics of deep neural networks (CNN, LSTM)
  • cut detection
  • image recognition
  • similarity search
  • image segmentation
  • person recognition
  • Text spotting

Qualification Goals

The learning objective of the module is to understand and be able to apply the methods necessary for the content-based analysis of image and video data. These include methods of image and moving image processing and machine learning. After visiting the module, the listeners should be able to design and implement software systems for image recognition based on Deep Learning libraries (Caffe, Tensorflow, ...). In addition, the students practice scientific working methods by training their ability to abstract as well as recognizing, formulating and solving problems.


Translation is missing. Here is the German original:

Keine. Empfohlen werden die Kompetenzen, die in den Basismodulen zur Praktischen Informatik vermittelt werden. Darüber hinaus ist Programmiererfahrung in Python und C++ empfehlenswert und Grundkenntnisse in Linux sind hilfreich.


Module imported from M.Sc. Computer Science.

It can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science

When studying B.Sc. Computer Science, this module can be attended in the study area Compulsory Elective Modules in Computer Science.

Die Wahlmöglichkeit des Moduls ist dadurch beschränkt, dass es der Praktischen Computer Science zugeordnet ist.

Recommended Reading

  • Will be announced in the course.

Please note:

This page describes a module according to the latest valid module guide in Summer semester 2018. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.